Value Premium

Has (hypothetical) equity factor investing worked as well in recent years as indicated in past studies? In his July 2015 paper entitled “Factor Investing Revisited”, David Blitz updates his prior study quantifying the performance of allocations to U.S. stocks based on three factor premiums: (1) value (high book-to-market ratio); (2) momentum (high return from 12 months ago to one month ago); and, (3) low-volatility (low standard deviation of total returns over the last 36 months). He considers two additional factor allocations: (4) operating profitability (high return on equity); and, (5) investment (low asset growth). He specifies each factor portfolio as the 30% of U.S. stocks with market capitalizations above the NYSE median that have the highest expected returns, reformed monthly for momentum and low-volatility and annually for the other factors. He considers both equal-weighted and value-weighted portfolios for each factor. He also summarizes recent research on the role of small-capitalization stocks, factor timing, long-only versus long-short portfolios, applicability to international stocks and applicability to other asset classes. Using value, momentum, profitability and investment factor portfolio returns from Kenneth French’s library and low-volatility portfolio returns as constructed from a broad sample of U.S. stocks during July 1963 through December 2014, he finds that: Keep Reading

Do factors that predict returns in U.S. stock data also work on global stock markets at the country level? In the May 2015 version of their paper entitled “Do Quantitative Country Selection Strategies Really Work?”, Adam Zaremba and Przemysław Konieczka test 16 country stock market selection strategies based on relative market value, size, momentum, quality and volatility. For each of 16 factors across these categories, they sort country stock markets into fifths (quintiles) and measure the factor premium as return on the highest minus lowest quintiles. They consider equal, capitalization and liquidity (average turnover) weighting schemes within quintiles. They look at complementary large and small market subsamples, and complementary open (easy to invest) and closed market subsamples. Using monthly total returns adjusted for local dividend tax rates in U.S. dollars for 78 existing and discontinued country stock indexes (primarily MSCI) during 1999 through 2014, they find that: Keep Reading

What is the best way to implement a value strategy for U.S. stocks? In their May 2015 paper entitled “Optimizing Value”, Ran Leshem, Lisa Goldberg and Alan Cummings investigate how the choice of value metric and implementation approach affect value strategy performance. They first compare book value-to-price ratio (B/P) and earnings-to-price ratio (E/P) based on returns for portfolios of the top 30% of stocks based on each metric, reformed frictionlessly each month since 1951. They then compare practical implementations that reform portfolios of S&P 500 stocks quarterly since 1973 (with round-trip trading friction 0.12%) by: (1) selecting the top 30% stocks based on the value metrics; or, (2) tilting S&P 500 Index weights based on the metrics. Finally, they add constraints to avoid value portfolio sector concentrations. Using Ken French’s value factor data since 1951 and data for S&P 500 stocks since 1973, both through December 2013, they find that: Keep Reading

When does value investing work and how does it work best? In the April 2015 initial draft of their paper entitled “Fact, Fiction, and Value Investing”, Clifford Asness, Andrea Frazzini, Ronen Israel and Tobias Moskowitz address areas of confusion about value investing. They describe value as the tendency of cheap securities to outperform expensive ones based on some valuation method. They broadly specify the value premium as the return achieved by holding or overweighting cheap securities and shorting or underweighting expensive ones. They focus on systematic (mechanical), diversified value strategies based on quantified metrics such as book-to-market ratio or earnings-price ratio. Their context is firm belief that such strategies are great investments. Based on academic studies and simple tests with recent data, largely from Kenneth French’s data library, they conclude that: Keep Reading

Are positive carry and positive trend conditions consistently favorable across asset classes? In their March 2015 paper entitled “Carry and Trend in Lots of Places”, Vineer Bhansali, Josh Davis, Matt Dorsten and Graham Rennison employ futures prices to investigate whether the adages “don’t pay too much to hold an investment” and “don’t fight the trend” actually work across four major asset classes: equities, bonds, commodities and currencies. For testing, they select five liquid markets with relatively long futures histories within each asset class. They define carry as annualized excess return assuming that spot prices do not change. They define trend as positive (negative) if the futures price today is above (below) its one-year trailing moving average. They specify four states for each market:

1. Positive carry and positive trend (Carry + / Trend +).
2. Positive carry and negative trend (Carry + / Trend -).
3. Negative carry and positive trend (Carry – / Trend +).
4. Negative carry and negative trend (Carry – / Trend -).

They then calculate average subsequent daily excess returns for each market by state and annualize results. Using daily futures data as available and some simulated futures data (from spot prices) for 20 major markets across four asset classes during 1960 through 2014, they find that: Keep Reading

Do stock return anomalies exhibit January and month-of-quarter (first, second or third, excluding January) effects? In his February 2015 paper entitled “Seasonalities in Anomalies”, Vincent Bogousslavsky investigates whether the following 11 widely cited U.S. stock return anomalies exhibit these effects:

1. Market capitalization (size) – market capitalization last month.
2. Book-to-market – book equity (excluding stocks with negative values) divided by market capitalization last December.
3. Gross profitability – revenue minus cost of goods sold divided by total assets.
4. Asset growth – Annual change in total assets.
5. Accruals – change in working capital minus depreciation, divided by average total assets the last two years.
6. Net stock issuance – growth rate of split-adjusted shares outstanding at fiscal year end.
7. Change in turnover – difference between turnover last month and average turnover the prior six months.
8. Illiquidity – average illiquidity the previous year.
9. Idiosyncratic volatility – standard deviation of residuals from regression of daily excess returns on market, size and book-to-market factors.
10. Momentum – past six-month return, skipping the last month.
11. 12-month effect – average return in month t−k*12, for k = 6, 7, 8, 9, 10.

Each month, he sorts stocks into tenths (deciles) based on each anomaly variable and forms portfolios that are long (short) the decile with the highest (lowest) values of the variable. He updates all accounting inputs annually at the end of June based on data for the previous fiscal year. Using accounting data and monthly returns for a broad sample of U.S. common stocks during January 1964 to December 2013, he finds that: Keep Reading

Does the average mutual fund investor accrue the average fund performance, or do investor timing practices alter the equation? In their July 2014 paper entitled “Timing Poorly: A Guide to Generating Poor Returns While Investing in Successful Strategies“, Jason Hsu, Brett Myers and Ryan Whitby compare the average dollar-weighted and buy-and-hold returns of different U.S. equity mutual fund styles, with focus on the value style. Dollar weighting adjusts the return stream based on the timing and magnitude of fund flows and is a more accurate measure than buy-and-hold of the returns realized by fund investors who may trade in and out of funds. Using monthly returns, monthly total assets and quarterly fund style information for a broad sample of U.S. equity mutual funds during 1991 through 2013, they find that: Keep Reading

Does adding profitability and asset growth (investment) factors improve the performance of the widely used Fama-French three-factor (market, size, book-to-market) model of stock returns? In the September 2014 version of their paper entitled “A Five-Factor Asset Pricing Model” Eugene Fama and Kenneth French assess whether extensions of their three-factor model to include profitability and investment improves model predictive power. They measure profitability as prior-year revenue minus cost of goods sold, interest expense and selling, general and administrative expenses divided by book equity. They define investment as prior-year growth in total assets divided by total assets. Using returns and stock/firm characteristics for a broad sample of U.S. stocks during July 1963 through December 2013 (606 months), they find that: Keep Reading

Do predictive powers of the size, value and momentum factors observed for individual stocks translate to the country level? In the November 2014 version of his paper entitled “Country Selection Strategies Based on Value, Size and Momentum”, Adam Zaremba investigates country-level value, size and momentum premiums, and tests whether the value and momentum premiums are equally strong across markets of different sizes and evaluates a country-level multi-factor asset pricing model. He measures factors at the country level as:

• Value: aggregate book-to-market ratio, with aggregate 12-month earnings-to-price-ratio, cash flow-to-price ratio and dividend yield as alternatives where available.
• Size: total market capitalization of country stocks.
• Momentum: cumulative return over preceding 12, 9, 6 or 3 months excluding the last month to avoid short-term reversal.

He relies on capitalization-weighted, U.S. dollar-denominated gross total return MSCI equity indexes as available, with Dow Jones and STOXX indexes as fallbacks (an average 56 indexes per month over time). He includes discontinued country indexes. He uses one-month LIBOR as the risk-free rate. Each month, he ranks countries by value, size and momentum into value-weighted or equal-weighted fifths (quintiles). He also performs double-sorts first on size and then on value or momentum. Using monthly firm/stock data for listed stockswithin 78 country indexes as available during February 1999 through September 2014 (147 months), he finds that: Keep Reading

Are gains from tax-loss harvesting, the systematic taking of capital losses to offset capital gains, additive to or subtractive from premiums from portfolio tilts toward common factors such as value, size, momentum and volatility (smart beta)? In their October 2014 paper entitled “Factor Tilts after Tax”, Lisa Goldberg and Ran Leshem look at the effects on portfolio performance of combining factor tilts and tax-loss harvesting. They call the incremental return from tax-loss harvesting tax alpha, which (while investor-specific) is typically in the range 1%-2% per year for wealthy investors holding broad capitalization-weighted portfolios. They test six long-only factor tilts based on Barra equity factor models: (1) value (high earnings yield and book-to-market ratio); (2) momentum (high recent past return); (3) value/momentum; (4) small/value; (5) quality (value stocks with low earnings variability, leverage and volatility); and, (6) minimum volatility/value (low volatility with diversification constraint and value tilt). Their overall benchmark is the MSCI All Country World Index (ACWI). Their tax alpha benchmark derives from a strategy that harvests losses in a capitalization-weighted portfolio (no factor tilts) without deviating far from the overall benchmark. The rebalancing interval is monthly for all portfolios. Using monthly returns for stocks in the benchmark index during January 1999 through December 2013, they find that: Keep Reading